Much effort has been made to deliver live broadcast services over the past decade. In the early days, live content broadcast was built over IP multicast systems. However, as IP multicast faces practical deployment and management issues, it is only used in limited scenarios such as Internet Service Provider (ISP) oriented Internet Protocol Television (IPTV) services.
After the year 2000, P2P-based live broadcasting systems won popularity to share video contents among end-user devices. P2P live streaming systems scale well under dynamic workloads, since each joining user acts as a reflected content source. However, P2P live streaming suffers from unstable video quality and severe latency up to minutes mainly due to peer churn and limited uplink bandwidth. In addition, P2P systems introduce significant user-side complexities and no longer fit the modern requirement of lightweight client implementation. Even though recent effort in augmenting P2P with cloud and content delivery network (CDN) can effectively reduce the latency and improve the streaming quality, the P2P nature makes it difficult for these systems to attract users preferring to watch live streams in a browser or on mobile devices.
Emerging commercial live content broadcasting platforms are facing great challenges to accommodate large scale dynamic viewer populations. On the one hand, more traditional TV programs, such as nightly news and sports games, are now streamed online in higher quality. Popular programs can easily attract millions of viewers. On the other hand, the emerging User-Generated Live Content (UGLC) are gaining tremendous popularity through various streaming platforms (such as Twitch, Facebook Live, and YouTube Live, etc.) and at the same time bringing new challenges. Any random UGLC may suddenly become viral on social media as the result of social cascading and recommender promotion, and cause a flash crowd of viewers to watch the same content within a few minutes. Without geographic and network distributions of the viewers, it is difficult to provision streaming resources to accommodate such unpredictable flash crowds in advance. Existing solutions constantly suffer from balancing the cost of deploying at the edge close to the viewers, the quality of content delivery, and the ability to rapidly scale in the face of near instantaneous, large viewer demand.
Various solutions to improve CDN performance for live broadcasting have been proposed. For instance, VDN developed a centralized streaming optimization and a hybrid control plane to reduce the startup latency and improve routing choices across different CDN clusters. Footprint shows the benefits of delivering streaming services by jointly optimizing the data center to provide the service, wide area network (WAN) transport connectivity and the proxy selection. C3 proposes to improve video quality by helping clients to select better CDN sites through data-driven analysis.
Moreover, many live streaming platforms encourage interactions between content generators and viewers. For instance, Twitch offers viewers a chat box to send feedbacks to the broadcasters, while Facebook Live enables viewers to click emoji buttons while watching a broadcast. Such interactive features require UGLC streaming to have the minimized latency. Traditional CDN-based live broadcasting systems are incapable of meeting all the new demands CDN providers aggregate to one or multiple data centers to take advantage of the elasticity of Virtual Machine (VM) resources and the flexibility of routing inside data centers. However, the lack of edge presence makes the streaming vulnerable to long playback lag and congestion fluctuations in Wide Area Networks (WAN). Leading live streaming platforms like YouTube and Twitch can suffer from occasional service interruption and unstable video quality.